Linear periodization
Linear periodization is a periodization model in which one or more training variables change gradually in a general direction across successive phases. A common resistance-training example starts with relatively more volume and lighter loads, then moves toward less volume and heavier loads as a strength test or competition approaches.
"Linear" describes the broad sequence, not an unbroken increase in performance. A four-week phase might use moderate loads and higher repetition targets, followed by a phase with fewer repetitions and heavier loads. Individual workouts can still fluctuate, and the plan can include recovery weeks. The direction may also differ when the goal is not peak maximal strength.
Linear periodization should not be confused with linear progression. Linear progression usually applies a simple performance rule, such as adding 2.5 kg after completing all prescribed repetitions. Linear periodization changes the planned emphasis across a longer sequence, whether or not load rises after every successful workout. It is also broader than load periodization, because volume, repetition ranges, exercises, and effort may change together.
For example, a lifter could spend one mesocycle doing sets of 8–10, a second doing sets of 5–7, and a final phase practicing heavier sets of 2–4. That outline does not specify a complete program: weekly set volume, proximity to failure, exercise selection, recovery, and the athlete’s response still determine the actual training demand. Merely lowering repetitions while adding weight is not automatically appropriate periodization.
Linear models can provide a clear path toward a dated performance goal, but they are not mandatory for general strength or hypertrophy training. In a 2022 volume-equated meta-analysis, periodized training improved one-repetition-maximum strength more than nonperiodized training on average, while undulating models outperformed linear models only in the subgroup of trained participants; hypertrophy did not differ meaningfully. This does not make linear periodization ineffective. It shows that model choice should reflect the outcome, training history, schedule, and need for specificity rather than a universal hierarchy.